Remote Coastal Weed Infestation Management Using Bayesian Networks

• Published in: Diversity (Basel) Vol. 16; no. 7; p. 382
• Main Authors: Kininmonth, Stuart, Spencer, Kerry, Hill, Amie, Sjerp, Eric, Bangay, Jethro
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Algorithms; Bayesian analysis; Bayesian Networks; Biodiversity; biodiversity management; Climate change; Climatic changes; Coastal ecology; Coastal management; coastal systems; Colonization; Dunes; Ecosystems; Environmental conditions; Feedback; Geospatial data; Global climate; Ground operations; Mathematical models; Metabolism; National parks; National parks and reserves; Prediction models; Predictions; Probability; Rainfall; Regional development; Regions; Remote control; Sand dunes; Temperature requirements; Weed control; Weeds; Australia
• ISSN: 1424-2818; 1424-2818
• DOI: 10.3390/d16070382

The increasing prevalence of species that are detrimental to biodiversity is a major concern, particularly for managers of national parks. To develop effective programmes for controlling weeds, it is essential to have a thorough understanding of the extent and severity of infestations, as well as the contributing factors such as temperature, rainfall, and disturbance. Predicting these factors on a regional scale requires models that can incorporate a wide range of variables in a quantifiable manner, while also assisting with on-ground operations. In this study, we present two Bayesian Network models specifically designed for six significant weed species found along the southern coast of Australia. Our models are based on empirical data collected during a coastal weed survey conducted in 2015 and repeated in 2016. We applied these models to the coastal national parks in the isolated and pristine East Gippsland region. Importantly, the prediction models were developed at two different spatial scales that directly corresponded to the scale of the observations. Our findings indicate that coastal habitats, with their vulnerable environments and prevalence of open dune systems, are particularly susceptible to weed infestations. Moreover, adjacent regions also have the potential for colonization if these infestations are not effectively controlled. Climate-related factors play a role in moderating the potential for colonization, which is a significant concern for weed control efforts in the context of global climate change.